Artificial Intelligence and Machine Learning to Prevent Injury and Enhance Performance in Sports

Photo by Markus Spiske on Unsplash

Injuries cost over 1 billion dollars to Major League Baseball teams, and thus, preventing them is of high priority in that sport too.

Artificial intelligence (AI) concepts and methods have sparked a lot of interest in the information industry and in society as a whole, owing to the enormous number of applications, the massive volume of data and the impending necessity to transform its data into actionable insights and solutions.

However, in some domains, such as sports, the efficient use of data is still being developed. In all types of sports, as in most other areas of life, an increasing amount of data has been collected, and automated data analysis has become an essential and rapidly rising profession.
Rigorous examination of these enormous data sets can improve our understanding of sports sciences while also assisting practitioners in their decision-making when it comes to training and competition strategy improvement.

Data science has developed as a critical field for leveraging sports science expertise and filling some of the gaps left by traditional statistical methods. Data science, as a hybrid knowledge area, is more than a mix of statistics and computer science since it necessitates expertise in how to weave statistical and computational methodologies into a bigger framework, problem by problem, and to answer discipline-specific questions.

Understanding the context of data, realising the responsibilities associated with accessing private and public data, and clearly communicating what a dataset can and cannot tell us about the real world, in our case, the sports world, are all essential components of a comprehensive view of data science.

The algorithms can be tweaked and enhanced based on learning models in order to deliver better outcomes for supporting decisions and providing practical information to athletes and sports professionals. These algorithms are used for both supervised and unsupervised learning (e.g., classification and regression) (e.g., clustering).

In order to construct a predictive model, supervised learning requires both input and output data, whereas unsupervised learning relies just on input data.

From the perspective of the sports science and medicine staff, however, success in a team sport is defined by the successful application of evidence-based knowledge to improve the decision-making process for injury risk reduction and athlete performance optimization. How can research findings and innovations be better adopted, thereby increasing player and coach/manager compliance with injury prevention and performance enhancement programmes?

To address this topic, we must agree that the requirements of the players come first and that the major stakeholders are the players and coaches. As a result, we must bring value to them by providing solutions that have a significant impact on their daily life.

The ability to predict injury risk and performance is a major issue in the sports sector. Many years of personal expertise have historically been credited to the coaching staff’s ability to prescribe exercise to attain optimal athletic performance with a low risk of injury. Modern approaches, on the other hand, that attempt to employ scientific methods for the effective construction of ideal training programmes are required. The use of modern statistical methodologies from AI opens up a fascinating new perspective for dealing with injury prevention and improving performance models.
As a result, research into the state-of-the-art of AI techniques or approaches applied to team sports is necessary.

With this in mind, the goal of this blog is to provide an overview of where AI is now being used in team sports.

This blog aims to answer the following questions in particular:

(1) What AI techniques have been used in research looking at injury risk and team sports performance?
(2) In which team sports have AI approaches been used to forecast injury risk and athlete performance?

What does Literature say?

Photo by Tamara Gak on Unsplash

In 12 team sports, eleven AI tools or methodologies were discovered. Approximately two-thirds of the AI studies (74 per cent) were on athletic performance, whereas 15 studies were about injury risk (26 per cent).
In terms of injury risk assessment studies, 27% dealt with training load, 13% with a concussion, screening, and training process/knee injury causes, and 7% each with ground reaction force pattern, heart defect detection, wearable sensor monitoring, psychosocial stress factors, and ulnar collateral ligament reconstruction approaches. The technical and tactical analysis accounted for 88 per cent of the performance studies, while physical, technical, and tactical analysis accounted for 5%, and match attendance, psychological dynamics of cooperative cooperation, and prediction based on heart rate accounted for 2% each.

Artificial neural networks have been the most widely used AI methodology or method for injury risk assessment and sporting performance prediction in the previous five years (10 per cent of the injury risk and 26 per cent of the sports performance prediction studies, reporting its use). The decision tree classifier and support vector machine (5 per cent) were the next most commonly utilised techniques and approaches in injury risk assessment research. The decision tree classifier (17%), Markov process (9%), and support vector machine (9%) were the most commonly employed AI techniques/methods in the performance prediction sector. Soccer (12%), basketball, American football, Australian football, and handball (3%) were the sports that employed AI for injury risk assessment, whereas basketball (19%), soccer (14%), and volleyball (9%) were the sports that used performance analytics the most algorithms for prediction.

image by Claudino et al (2019)

To predict injuries, AI was applied to “training load”, “training process/knee injury causes”, “heart defect detection”, “ground reaction force pattern”, “psychosocial stress factors”, and “screening” of which 43% were on the artificial neural network, 29% on decision tree classifier, and 14% on each of Bayesian logistic regression and least absolute shrinkage and selection operator. For injury risk prediction, the most frequently used AI technique or method applied in collegiate and professional basketball players was artificial neural network.

Ground reaction force patterns are a standard way of study in sports medicine and biomechanics that is also linked to knee injuries in multidirectional sports like handball and volleyball in terms of injury risk assessment. Jump landings, cutting, and rotating are all examples of activities that might result in these injuries. In addition, injury prediction using an AI approach or method based on “screening” and “training load” has been successful in players. When compared to the use of single screening tests, this technique may help in recognising the injury risk with a higher likelihood.

image by Claudino et al (2019)

The AI techniques or methods used to predict sporting performance were as follows: 25% each of artificial neural networks, Bayesian network and decision tree classifier, and 13% each of fuzzy clustering and K-means clustering.

There is always a tug of war between injury and performance, which that implies if injury incidence is more it can leads decrement in performance, how? It leads to affect that athlete in a multi-dimensional way such as loss of training, loss of competition, enhance stress and overall affects the performance.

Let’s assume:

y = performance

x1 = injury_incidence

x2 = ground_reaction_force

x3 = stress

y = x1 * w1 + x2 * w2 + x3 * w3 +……+ xn*wn

Performance is a complex outcome and it runs by multiple factors, tends to get affected by minute variation.

As a result, this blog demonstrated that artificial neural networks, decision tree classifiers, support vector machines, and the Markov process were the most commonly employed AI approaches or methodologies for forecasting injury risk and sporting performance in team sports. Soccer, basketball, handball, and volleyball were the team sports with the most AI applications.

Happy Learning….!!

Stay tuned for more….!!



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